{"id":"W2982528182","doi":"10.1109/tro.2019.2946746","title":"Robotic Manipulation of Deformable Cells for Orientation Control","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Artificial intelligence; Orientation (vector space); Oocyte; Robotics; Computer science; Pipette; Morphing; Computer vision; Geometry; Robot; Mathematics; Biology; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001510547,0.0003543971,0.0002644569,0.0001713727,0.0002096501,0.0003070283,0.0004967125,0.0003707821,0.001189232],"category_scores_gemma":[0.0002728902,0.0001666251,0.0002766663,0.0001451354,0.0003157239,0.0003009874,0.0004253219,0.0003456978,0.0004620069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004023036,"about_ca_system_score_gemma":0.0003622246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00141901,"about_ca_topic_score_gemma":0.001880485,"domain_scores_codex":[0.9997874,0.00001828432,0.000009366339,0.00005807985,0.00010839,0.00001852524],"domain_scores_gemma":[0.9998988,0.00002223043,0.0000293744,0.00002053276,0.00001946873,0.000009698112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005017517,0.00004051832,0.0004243246,0.00009924997,0.00001320064,0.000101463,0.00006945714,0.02879727,0.8505412,0.006712029,0.001385109,0.111766],"study_design_scores_gemma":[0.00002806767,0.000288553,0.001920417,0.00002947945,0.0000309758,0.0004067844,0.00004178052,0.4877475,0.4593008,0.003849237,0.04629148,0.00006491473],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05527817,0.001773686,0.9290669,0.0003166408,0.0002129068,0.00009736842,0.0000864844,0.00179806,0.01136972],"genre_scores_gemma":[0.6992617,0.001676104,0.2881273,0.0003757525,0.00009069125,0.0001560216,0.0002163613,0.0001012641,0.009994856],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00141901,"threshold_uncertainty_score":0.003978372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0117882823675627,"score_gpt":0.2050192760248856,"score_spread":0.1932309936573229,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}